Episode 8. This is the first in a series of episodes dedicated to model validation. Today, we focus on model robustness and resilience. From complex financial systems to why your gym might be overcrowded at New Year's, you've been directly affected by these aspects of model validation.
AI hype and consumer trust. 0:03
- FTC article highlights consumer concerns about AI's impact on lives and businesses. (Oct 3, 2023)
- Susan Peich notes that the public is becoming more aware of the use of AI in customer service calls and its potential implications, thanks to recent media coverage.
- Sid Mangalik agrees, citing the FTC's findings that people are increasingly concerned about AI's potential to be misused and the need for transparency and trust in AI's development and deployment.
Model validation and its importance in AI development. 3:42
- Andrew Clark emphasizes the importance of model validation in AI development, highlighting the need to ensure models are doing what they're supposed to do.
- Susan agrees, citing the FTC's heightened awareness around responsibility and the need for companies to be fair and unbiased in their AI practices.
- Andrew: Model validation is crucial to ensure the model is doing what it's supposed to do, but it's difficult when the model is supposed to do everything.
- Sid: Model verification is targeted and specific, unlike model evaluation which is more general and open-ended.
Model validation and resilience in machine learning. 8:26
- Engineers and businesses must collaborate to validate models for resilience and robustness.
- Resilience in model validation refers to how well a model handles adverse data scenarios, while robustness refers to its ability to generalize to unforeseen data.
- In Aerospace Engineering, models must be both resilient and robust to perform well in real-world environments, where data may be noisy or missing, and the model must generalize to new situations.
- Andrew Clark emphasizes the importance of validating machine learning models against physical properties, as they are based on correlations rather than causations.
- He highlights the challenges of using machine learning in aerospace engineering, where physical properties have been well-established through physics and mathematics.
Statistical evaluation and modeling in machine learning. 14:09
- Statistical evaluation in machine learning involves modeling distribution without knowing everything, using methods like Monte Carlo sampling.
- Monte Carlo simulations originated in physics for assessing risk and uncertainty in decision-making, now widely used in various fields.
Monte Carlo methods for analyzing model robustness and resilience. 17:24
- Monte Carlo simulations allow for exploration of potential input spaces and estimation of underlying distribution, even when analytical solutions are unavailable.
- Andrew and Sid discuss using Monte Carlo simulations to evaluate a representative dynamical system model, with sensitivity analysis and uncertainty analysis as two major flavors of analyses.
- They explore how changes in input parameters affect output outcomes, and use historical data to understand how outputs change over time.
Monte Carlo techniques and model validation. 21:31
- Andrew highlights the versatility of Monte Carlo simulations in various fields, including engineering, economics, and computer science.
- Sid suggests using Monte Carlo experiments to explore the semantic space vectors of language models like GPT, even if they are blackboxes.
- Sid and Andrew discuss the importance of validating machine learning models through negative scenario analysis, which involves testing the model's resilience to unexpected inputs or events.
- They emphasize the need to focus on stress testing and black swan events to see how well the model has learned about the world and its intrinsic properties.
Stress testing and resiliency in finance and engineering. 25:48
- Andrew emphasizes the importance of stress testing in finance, particularly for large banks, and how it can help identify the edges of a system that need to be improved.
- He suggests combining traditional stress testing with Monte Carlo methods to enhance the accuracy and robustness of the results.
- Sid discussed the importance of synthetic data in modeling critical systems, highlighting its potential to increase resilience in the face of major shocks.
- Andrew emphasized the need for stress testing in various fields, including engineering and finance, to identify potential gaps and vulnerabilities in critical systems.
Using operations research and model validation in AI development. 30:13
- Sid highlights the problem of overcrowding in gyms and how operations research can help find a happy equilibrium.
- Andrew agrees and adds that operations research has robust methods for solving complex problems in logistics and healthcare, but the ML community rarely looks to these disciplines for help.
- Susan and Andrew discuss the limitations of AI models and the importance of model validation, highlighting the need for robust and resilient models that can handle complex tasks.
- They also touch on the potential of model validation to provide hope for improving AI systems and addressing issues of bias and fairness in the future.
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